In this paper, a system for transferring human grasping skills to a robot is presented. In order to reduce the dimensionality of the grasp postures, we extracted three synergies from data on human grasping experiments and trained a neural network with the features of the objects and the coefficients of the synergies. Then, the trained neural network was employed to control robot grasping via an individually optimized mapping between the human hand and the robot hand. As force control was unavailable on our robot hand, we designed a simple strategy for the robot to grasp and hold the objects by exploiting tactile feedback at the fingers. Experimental results demonstrated that the system can generalize the transferred skills to grasp new objects.